Stop AI Bias Now: 10 Proven Strategies to Build Smarter, Fairer Models
How Leading Brands Are Tackling Bias Head-On to Build AI Systems That Inspire Trust and Drive Results.
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Intro: Hi, I’m Mukundan Sankar, a data professional with over seven years of experience in the field.
My journey into data wasn’t planned—it was a happy accident that began in 2013 when I stumbled upon data analytics while figuring out my next steps after earning a degree in Electronics and Telecommunications Engineering. What started as a curiosity quickly turned into a passion, leading me to pursue advanced data courses and officially kickstart my career in 2016. Today, I help businesses turn numbers into actionable insights, combining data with storytelling to make complex ideas accessible and impactful. I also host the Data & AI with Mukundan podcast, where I dive into topics like data, AI, productivity, and business. You can find it on Apple Podcasts, Spotify, and more. Alongside my podcast, I write on Substack, sharing practical insights and personal reflections on how data shapes our world. For me, working with data is about more than just analysis—it’s about creating connections, simplifying complexity, and finding purpose. I’m always eager to learn, grow, and connect with others who share a passion for data’s transformative power. Let’s explore what data can do together!
Artificial intelligence is rapidly reshaping industries. It has been offering the promise of smarter decisions, better insights, and untapped potential! However, it’s crucial to acknowledge a significant flaw many businesses overlook—bias. This is not a problem we can afford to businesses overlook—bias.
The AI Bias - it isn’t only an ethical concern. It’s a business risk that can't be ignored. Let me explain how! For a second, let's imagine we have a business and have customers belonging to different groups. For the sake of this argument, let's suppose they belong to different ethical groups. Our chatbot, a core aspect of our business, responds to different customers differently, but more specifically, it responds to other racial groups differently. We didn't do it intentionally, and maybe neither did the chatbot. It was just unintentional. Why could this be the case? We just didn't have enough data to train our chatbot, which caused it to discriminate. We will lose customer trust because our chatbot unintentionally discriminates or misses out on top talent, and our hiring algorithm unfairly filters out certain groups. If our AI isn’t fair, transparent, and ethical, We’re setting ourselves up for a PR nightmare—or worse, regulatory action.
So, how do We tackle this beast? How do We build AI systems that are smarter, better, and unbiased? Here are 10 proven strategies, backed by research and real-world examples, that we can implement to keep our AI models fair, ethical, and effective.
Audit our Data: Garbage In, Garbage Out
Bias often creeps into AI because of the data it’s trained on. I discussed this in the introduction: If we train our data in a way that doesn’t represent the real-world population, our model’s predictions will reflect those gaps. For instance, an AI model trained on a dataset predominantly featuring men might underperform when analyzing women’s data.
How to Do It:
Diversity Checks: Regularly analyze datasets to ensure they are diverse in gender, race, geography, and other key attributes.
Data Cleaning: Remove outdated, irrelevant, or skewed data that might misrepresent our target audience.
Example: A 2018 study found that facial recognition systems from significant tech companies performed poorly on darker-skinned individuals due to biased datasets. Addressing this required including more diverse facial images in their training data.
Source: Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
De-bias our Dataset with Re-sampling and Re-weighting
Sometimes, bias in data isn’t about the absence of diversity but the over-representation of certain groups. For example, if our training data has 80% of the dataset consisting of data from one demographic, our model will favor that group.
How to Do It:
Re-sampling: Adjust the dataset by oversampling underrepresented groups or undersampling overrepresented ones.
Re-weighting: Assign different weights to samples during training to ensure balanced representation.
Proven Technique: IBM has a tool called AI Fairness 360 that helps address bias in data by adjusting the influence different parts of the data have.
Source: IBM AI Fairness 360 Toolkit
Leverage Explainable AI (XAI)
Bias is often hidden in the complexity of black-box models. Explainable AI (XAI) allows us to see how decisions are made, enabling us to identify and address biases.
How to Do It:
To analyze our model's decision-making process, we can use tools like SHAP (SHapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations).
Visualize the importance of features to see which variables influence predictions the most.
Example: Google used explainability techniques to improve their search algorithms, identifying and mitigating biases in ranking systems.
Source: Explainable AI: Interpretable Machine Learning
Choose the Right Algorithm
Not all algorithms are created equal. Some models, like decision trees, are inherently easier to interpret, while others, like deep neural networks, can be more prone to unintended bias.
How to Do It:
Opt for interpretable algorithms, especially in high-stakes decisions (e.g., hiring or lending).
Regularly test different models and compare their fairness metrics.
Key Insight: Research in machine learning highlights that more straightforward, interpretable models often provide advantages in fairness-critical applications due to their transparency and lower risk of encoding unintended biases.
Source: Fairness in Machine Learning: Lessons from Political Philosophy
Incorporate Fairness Constraints
Modern AI frameworks allow us to bake fairness into the model training process by introducing constraints that ensure equitable outcomes.
How to Do It:
Use fairness-aware machine learning libraries like AIF360 or Fairlearn.
Set constraints such as equalized odds, demographic parity, or disparate impact reduction during model training.
Real-World Example: The case study below discusses how LinkedIn introduced fairness constraints in its job recommendation algorithms to ensure equal visibility for all demographics.
Source: Improving Fairness in Job Recommendations
Test for Bias Early and Often
The earlier we identify bias, the easier it is to fix. Testing our models regularly during development prevents minor issues from snowballing into major problems.
How to Do It:
Split our data into demographic groups and measure each group's model performance (accuracy, precision, recall).
Use fairness metrics like disparate impact ratio and equal opportunity difference.
Toolkit: The Fairlearn library provides metrics and visualization tools for bias testing during development.
Source: Fairlearn: A Toolkit for Assessing and Improving Fairness in AI
Bring Humans into the Loop
AI isn’t perfect, and automated systems can benefit from human oversight. Human-in-the-loop (HITL) approaches allow experts to review AI decisions and flag bias before deployment.
How to Do It:
Introduce manual review steps in high-risk areas (e.g., loan approvals or legal decisions).
Use active learning techniques where humans label ambiguous or high-impact cases for better model training.
Example: Amazon’s recruiting tool, which initially showed gender bias, was improved by integrating human oversight into the selection process.
Source: Amazon AI Recruiting Bias
Regularly Retrain Models with Fresh Data
The world changes, and so do the patterns in our data. Stale models that fail to adapt to new social and cultural norms are more likely to exhibit bias.
How to Do It:
Schedule periodic retraining with updated datasets.
Implement continuous learning pipelines to keep models in sync with real-world trends.
Insight: Studies from Stanford University emphasize the importance of updating AI models to reflect evolving societal norms.
Source: Lifespan and Drift in Machine Learning Models
Diversify our AI Development Team
Bias results from the perspectives of the people creating the system. Diverse teams bring different viewpoints, which can help reduce blind spots.
How to Do It:
Focus on hiring a diverse group for our AI and data science teams.
Regularly provide training on bias awareness for all team members.
Case Study: Microsoft’s AI development team’s diversity initiatives helped them create more inclusive products. One such product was Seeing AI, an app for visually impaired users.
Source: Diversity and Inclusion in AI Development
Monitor and Audit our Models Post-Deployment
Bias isn’t just a one-time thing that we do. Our models need regular monitoring to catch and correct unforeseen issues even after deployment.
How to Do It:
We must set up performance dashboards to track key fairness metrics over time.
We need to conduct third-party audits to validate the fairness of our AI systems.
Industry Example: Twitter implemented ongoing audits of their image-cropping algorithm after users flagged racial bias.
Source: Bias in Twitter’s Image Cropping Algorithm
The Bottom Line: Building AI That Inspires Trust
Reducing AI bias isn’t just about ethics; it’s about building systems humans can trust! We now live in a world where decisions made by AI affect our daily lives. So, fairness in AI is no longer an option for a Business. It is a basic need if we want a thriving business! We need to keep our stakeholders happy after all!
By auditing our data and explaining clearly what we include or not especially around fairness requirements, we can create AI models that are not only powerful but also just Fair! We need to remember, the goal isn’t perfection—it’s progress. Every step toward reducing bias is a step toward AI that truly benefits everyone.
Ready to start? Let’s make AI fair, one model at a time.
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